Biomedical subjects

Xiaoli Chen

Publications and source records attributed to Xiaoli Chen.

4 recordsLinked to original sources

PubMed2026

[Genetic and phenotypic analysis of three children with Neurodevelopmental disorders due to variants of DEAF1 gene].

OBJECTIVE: To explore the genetic characteristics and clinical phenotypes of three children with novel DEAF1 gene variants. METHODS: Three children who were referred to Capital Children's Medical Center Affiliated to Capital Medical University between January 2018 and December 2025 were selected as study subjects and underwent whole exome sequencing (WES). Candidate variants were verified by Sanger sequencing, and their pathogenicity was evaluated based on the guidelines from American College of Medical Genetics and Genomics (ACMG). A systematic search of databases including PubMed and CNKI was conducted to compile previously reported cases of DEAF1 variants for clinical phenotype comparison. For the non-canonical splice site variant c.870+5G>C located in the intronic region, wild-type and mutant minigene reporter vectors were constructed and transfected into HeLa and 293T cells, respectively, and the splicing patterns were analyzed by RT-PCR and Sanger sequencing. This study was approved by the Medical Ethics Committee of Capital Institute of Pediatrics (Ethics No.: SHERLL 2020001). RESULTS: All three children were found to have carried de novo heterozygous variants of the DEAF1 gene, including two missense variants (c.764G>A, c.641T>C) in the important SAND domain and a splice site variant (c.870+5G>C) in a non-canonical splicing region. The c.764G>A and c.870+5G>C variants were unreported previously. All children had presented with intellectual developmental delay, and two were accompanied by autism spectrum disorder, and two had epilepsy and sleep disorders. In vitro minigene splicing assay showed that the c.870+5G>C variant can lead to abnormal splicing. CONCLUSIONS: This study reported three children with novel DEAF1 variants, two of which have not been previously described, thereby enriched the mutational spectrum of the DEAF1 gene. In vitro functional assay combined with the clinical manifestations of the patients confirmed the pathogenicity of the non-canonical splice site variant in the intronic region.

Humans↗

PubMed2026

Dual HBV cccDNA-linked HiBiT reporter hepatocyte models for screening of candidate cccDNA modulators.

Chronic hepatitis B remains difficult to cure because the viral covalently closed circular DNA (cccDNA) minichromosome can persist and sustain viral transcription, creating a need for scalable, reporter readouts that facilitate early discovery of cccDNA-modulating agents. Here, we developed two complementary hepatocyte HiBiT reporter models: a replication-competent HBV reporter in HepaRG cells (HepaRG-Hibit16), in which a secreted split-NanoLuc HiBiT signal is linked to cccDNA-associated expression, and a Cre/Lox-based recombinant cccDNA (rcccDNA) reporter in HepG2 cells (HepG2-Rccc1a) that rapidly generates rcccDNA with a matched HiBiT readout. Screening of 1,403 FDA-approved compounds across both models identified 13 concordant, non-cytotoxic hits. Palovarotene, a retinoic acid receptor-γ agonist, was selected as an exemplar concordant hit and reduced HBV antigens, HBV DNA, and cccDNA and inhibited HBV infection in multiple hepatocyte-based in vitro systems without overt cytotoxicity at the tested concentrations. Together, this dual-reporter strategy supports efficient cross-model triage of candidate cccDNA modulators for subsequent orthogonal validation.

Humans↗

PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗

PubMed2025

A pluripotent stem cell atlas of multilineage differentiation.

Human pluripotent stem cells offer a scalable platform to study genetic and signalling mechanisms governing cell lineage decisions during differentiation. Genome-wide and single-cell transcriptomics technologies likewise offer high-throughput analysis of heterogeneous cell differentiation states. While in vivo development has been extensively characterised using these technologies, there remains a need for comprehensive single-cell transcriptomic profiling of stem cell differentiation from pluripotency. Understanding gene expression changes governing differentiation in vitro is key to developing high fidelity differentiation protocols and understanding fundamental mechanisms of development. We generated a single-cell RNA sequencing time course to study the role of developmental signalling pathways on multilineage diversification from pluripotency in vitro. The combined dataset of over 60,000 cells spans cell types from a time course of differentiation across all germ layers, ranging from gastrulation cell states to progenitor and committed cell types. These data provide a diverse benchmarking reference point to compare against in vivo development and advance understanding of signalling regulation of differentiation, providing insights into protocol development, drug screening, and regenerative medicine applications.

Pluripotent Stem Cells↗